Power Management of Intelligent Buildings Facilitated by Smart Grid: A Market Approach

The emergence of smart grid technology is offering a unique opportunity to improve power management in intelligent buildings through the integration and optimization of distributed energy resources and loads. In this paper, the interactions of multiple intelligent buildings in the context of energy market, as well as distributed energy generation and storage facilities, is considered. Within a time horizon divided into multiple periods in which generations and loads are forecasted, each building in a certain period may experience power surplus or deficit. While any deficit can be obtained from the market, buildings may consider selling their unused power back to the market. A dynamic pricing model based on differential game theory is set up in order to study the interactions of these players and how they maximize their profit. We also propose algorithms to implement and operate the system over the time horizon considered. Furthermore, numerical studies are performed to validate the model and algorithms.

[1]  Ngo Van Long,et al.  Differential Games in Economics and Management Science: List of tables , 2000 .

[2]  Mariesa L. Crow,et al.  Pricing and Control in the Next Generation Power Distribution System , 2012, IEEE Transactions on Smart Grid.

[3]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[4]  Yi Ding,et al.  Real-Time Market Concept Architecture for EcoGrid EU—A Prototype for European Smart Grids , 2013, IEEE Transactions on Smart Grid.

[5]  Shigeru Kimura,et al.  Analysis on Price Elasticity of Energy Demand in East Asia: Empirical Evidence and Policy Implications for ASEAN and East Asia , 2014 .

[6]  A. David,et al.  Optimal bidding strategies and modeling of imperfect information among competitive generators , 2001 .

[7]  Yong Huat Chew,et al.  Oligopolistic spectrum allocation game via market competition under spectrum broker , 2014, Comput. Networks.

[8]  Andreas T. Ernst,et al.  Using intelligent storage to smooth wind energy generation , 2009 .

[9]  Qing-Shan Jia,et al.  Energy-Efficient Buildings Facilitated by Microgrid , 2010, IEEE Transactions on Smart Grid.

[10]  Young M. Lee,et al.  An inventory control and pricing model for smart building load management , 2014, ISGT 2014.

[11]  James Bushnell,et al.  Oligopoly equilibria in electricity contract markets , 2005 .

[12]  Bruno Francois,et al.  Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications , 2011, IEEE Transactions on Industrial Electronics.

[13]  Boon-Hee Soong,et al.  Dynamic market for distributed energy resources in the Smart Grid , 2014, 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC).

[14]  H. B. Gooi,et al.  Sizing of Energy Storage for Microgrids , 2012, IEEE Transactions on Smart Grid.

[15]  Andreas Sumper,et al.  Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets , 2013 .

[16]  Long Bao Le,et al.  Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics , 2014, IEEE Transactions on Smart Grid.

[17]  Jose B. Cruz,et al.  Bidding strategies in dynamic electricity markets , 2005 .

[18]  Ajinkya Vilas Sawant,et al.  GreenCharge: Managing Renewable Energy in Smart Buildings , 2017 .

[19]  Jim Kurose,et al.  GreenCharge : Managing Renewable Energy in Smart Buildings , 2012 .

[20]  Sonia Martínez,et al.  Storage Size Determination for Grid-Connected Photovoltaic Systems , 2011, IEEE Transactions on Sustainable Energy.

[21]  Cameron W. Potter,et al.  Building a smarter smart grid through better renewable energy information , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[22]  Vincent W. S. Wong,et al.  Direct Electricity Trading in Smart Grid: A Coalitional Game Analysis , 2014, IEEE Journal on Selected Areas in Communications.

[23]  B. Hobbs,et al.  An oligopolistic power market model with tradable NO/sub x/ permits , 2005, IEEE Transactions on Power Systems.

[24]  Yuguang Fang,et al.  A Market Based Scheme to Integrate Distributed Wind Energy , 2013, IEEE Transactions on Smart Grid.

[25]  Kelly Ann Gaydos Negative Prices in Competitive Electricity Markets , 2004 .

[26]  Jim Kurose,et al.  GreenCharge: Managing RenewableEnergy in Smart Buildings , 2013, IEEE Journal on Selected Areas in Communications.

[27]  Javier Contreras,et al.  Modeling the Impact of a Wind Power Producer as a Price-Maker , 2014, IEEE Transactions on Power Systems.

[28]  Sharanya Eswaran,et al.  Strategy and modeling for building DR optimization , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[29]  N. Hatziargyriou,et al.  Making microgrids work , 2008, IEEE Power and Energy Magazine.

[30]  Long Bao Le,et al.  Optimal energy management for building microgrid with constrained renewable energy utilization , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[31]  Alfonso Damiano,et al.  Real-Time Control Strategy of Energy Storage Systems for Renewable Energy Sources Exploitation , 2014, IEEE Transactions on Sustainable Energy.

[32]  Sungjin Lee,et al.  Joint Energy Management System of Electric Supply and Demand in Houses and Buildings , 2014, IEEE Transactions on Power Systems.

[33]  David W. K. Yeung,et al.  Cooperative Stochastic Differential Games , 2005 .

[34]  Yunsi Fei,et al.  Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents , 2015, IEEE Transactions on Smart Grid.

[35]  Robert Lasseter,et al.  Smart Distribution: Coupled Microgrids , 2011, Proceedings of the IEEE.